Reconstructing the world in 3D

Tuite, Kathleen; Snavely, Noah; Hsiao, Dun-Yu; Smith, Adam M.; Popović, Zoran

doi:10.1145/1822348.1822379

Cited by 24 publications

(2 citation statements)

References 7 publications

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“…The collected data is subjected to in-game validation by other players and once validated can be used for navigation tasks. PhotoCity is another GWAP where players contribute photos of real world locations in urban areas (Tuite et al, 2010(Tuite et al, , 2011. The ultimate goal is to use the collected photos to build detailed 3D models of real world places varying over time.…”

Section: Related Workmentioning

confidence: 99%

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Gligorov

Hildebrand

Ossenbruggen

et al. 2017

HCj

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Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. We find that Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In this study we evaluate to what extent the tags entered by players can be regarded as topical descriptors of the video material. Moreover, we characterize the quality of the user tags as topical descriptors with the aim to detect and filter out the bad ones. Our results show that after filtering, game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

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Section: Related Workmentioning

confidence: 99%

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Gligorov

Hildebrand

Ossenbruggen

et al. 2017

HCj

View full text Add to dashboard Cite

show abstract

“…Over the past decade, driven by increasingly cheap and easy methodologies to query and analyze information from the human crowd, a variety of Internet-enabled crowd-sourcing projects have demonstrated the ability to combine large numbers of human responses for a variety of difficult image analysis tasks [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . One of the most well-known and successful of these approaches is reCAPTCHA 2 , a system used as a human extraction system for digitizing books or extracting alphanumeric characters from images.…”

Section: Introductionmentioning

confidence: 99%

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians

et al. 2015

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Over the past decade, crowd-sourcing complex image analysis tasks to a human crowd has emerged as an alternative to energy-inefficient and difficult-to-implement computational approaches. Following this trend, we have developed a mathematical framework for statistically combining human crowd-sourcing of biomedical image analysis and diagnosis through games. Using a web-based smart game (BioGames), we demonstrated this platform's effectiveness for telediagnosis of malaria from microscopic images of individual red blood cells (RBCs). After public release in early 2012 (http://biogames.ee.ucla.edu), more than 3000 gamers (experts and non-experts) used this BioGames platform to diagnose over 2800 distinct RBC images, marking them as positive (infected) or negative (non-infected). Furthermore, we asked expert diagnosticians to tag the same set of cells with labels of positive, negative, or questionable (insufficient information for a reliable diagnosis) and statistically combined their decisions to generate a gold standard malaria image library. Our framework utilized minimally trained gamers' diagnoses to generate a set of statistical labels with an accuracy that is within 98% of our gold standard image library, demonstrating the "wisdom of the crowd". Using the same image library, we have recently launched a web-based malaria training and educational game allowing diagnosticians to compare their performance with their peers. After diagnosing a set of ~500 cells per game, diagnosticians can compare their quantified scores against a leaderboard and view their misdiagnosed cells. Using this platform, we aim to expand our gold standard library with new RBC images and provide a quantified digital tool for measuring and improving diagnostician training globally.

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Fight or unite: Investigating game genres for image tagging

Goh

Ang

Lee

et al. 2011

J. Am. Soc. Inf. Sci.

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Applications that use games to harness human intelligence to perform various computational tasks are increasing in popularity and may be termed human computation games (HCGs). Most HCGs are collaborative in nature, requiring players to cooperate within a game to score points. Competitive versions, where players work against each other, are a more recent entrant, and have been claimed to address shortcomings of collaborative HCGs such as quality of computation. To date, however, little work has been conducted in understanding how different HCG genres influence computational performance and players' perceptions of such. In this paper we study these issues using image tagging HCGs in which users play games to generate keywords for images. Three versions were created: collaborative HCG, competitive HCG, and a control application for manual tagging.The applications were evaluated to uncover the quality of the image tags generated as well as users' perceptions. Results suggest that there is a tension between entertainment and tag quality. While participants reported liking the collaborative and competitive image tagging HCGs over the control application, those using the latter seemed to generate better quality tags. Implications of the work are discussed.

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Reconstructing the world in 3D

Cited by 24 publications

References 7 publications

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians

Fight or unite: Investigating game genres for image tagging

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